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Surrogate models are extensively used to substitute real models which are expensive to evaluate in the time-dependent reliability analysis. Normally, different surrogate models have different scopes of application. However, information is often insufficient for analysts to select the most appropriate surrogate model for a specific application. Thus, the result precited by individual surrogate model tends to be suboptimal or even inaccurate. Ensemble model can effectively deal with the above concern. This work aims to study the application of ensemble model for reliability analysis of time-independent problems.

In this work, a method of reliability analysis for time-dependent problems based on ensemble learning of surrogate models is developed. The ensemble of surrogate models includes Kriging, radial basis function, and support vector machine. The prediction is approximated by the weighted average model. The ensemble learning of surrogate models is updated by finding and adding the sample points with large prediction errors throughout the entire procedure.

The effectiveness of the proposed method is verified by several examples. The results show that the ensemble of surrogate models can effectively propagate the uncertainty of time-varying problems, and evaluate the reliability with high prediction accuracy and computational efficiency.

This work proposes an adaptive learning framework for the uncertainty propagation of time-dependent problems based on the ensemble of surrogate models. Compared with individual surrogate models, the ensemble model not only saves the effort of selecting an appropriate surrogate model especially when the knowledge of unknown problem is lacking, but also improves the prediction accuracy and computational efficiency.

Based on the aerodynamic loads and dynamic performances of trains, this study aims to investigate the effect of crosswinds and raindrops on intercity trains operating on viaducts to ensure the safe operation of intercity railways in metropolitan areas.

An approach coupled with the Euler multiphase model as well as the standard k-ɛ turbulence model is used to investigate the coupled flow feature surrounding trains and viaducts, including airflow and raindrops, and the numerical results are validated with those of the wind tunnel test. Additionally, the train’s dynamic response and the operating safety region in different crosswind speeds and rainfall is investigated based on train’s aerodynamic loads and the train wheel–rail dynamics simulation.

The aerodynamic loads of trains at varying running speeds exhibit an increasing trend as the increase of wind speed and rainfall intensity. The motion of raindrop particles demonstrates a significant similarity with the airflow in wind and rain environments, as a result of the dominance of airflow and the supplementary impacts of droplets. As the train’s operating speed ranged between 120 and 200 km/h and within a rainfall range of 20–100 mm/h, the safe operating region of trains decreased by 0.56%–7.03%, compared with the no-rain condition (0 mm/h).

The impact of crosswind speeds and rainfall on the train’s aerodynamic safety is studied, including the flow feature of crosswind and different particle-sized raindrops around the train and viaduct, aerodynamic loads coefficients suffered by the intercity train as well as the operating safety region of intercity trains on the viaduct.

The purpose of this study is to extend the classical noncentral F-distribution under normal settings to noncentral closed skew F-distribution for dealing with independent samples from multivariate skew normal (SN) distributions.

Based on generalized Hotelling's T² statistics, confidence regions are constructed for the difference between location parameters in two independent multivariate SN distributions. Simulation studies show that the confidence regions based on the closed SN model outperform the classical multivariate normal model if the vectors of skewness parameters are not zero. A real data analysis is given for illustrating the effectiveness of our proposed methods.

This study’s approach is the first one in literature for the inferences in difference of location parameters under multivariate SN settings. Real data analysis shows the preference of this new approach than the classical method.

For the real data applications, the authors need to remove outliers first before applying this approach.

This study’s approach may apply many multivariate skewed data using SN fittings instead of classical normal fittings.

This paper is the research paper and the authors’ new approach has many applications for analyzing the multivariate skewed data.

This paper aims to examine the relationships between the group affiliates’ dual legitimacy (membership legitimacy and societal legitimacy) and dual resource acquisition (intra-group and out-group), and the moderating roles of environmental uncertainty and munificence in the emerging economies.

This paper adopts hierarchical regression analysis to test the hypotheses based on the unique data of 251 group affiliated firms in China and applies the alternative measurements and alternative methodology of structural equation modeling into robustness check to confirm the results.

The results show as follows: the group affiliates can benefit from membership legitimacy for intra-group resource acquisition and out-group resource acquisition through the mediations of societal legitimacy and intra-group resource acquisition. However, in the linkage between affiliates’ membership legitimacy and intra-group resource acquisition and the linkage between societal legitimacy and out-group resource acquisition, environmental uncertainty plays the positive moderating roles while environmental munificence plays the negative moderating roles. Under the condition of high environmental uncertainty and low environmental munificence, the linkage between membership legitimacy and intra-group resource acquisition, and the linkage between societal legitimacy and out-group resource acquisition reach the strongest level.

The findings highlight the importance of dual legitimacy building for group affiliates to acquire resources both inside and outside the business group when they operate in emerging economies characterized by high environmental uncertainty and low environmental munificence. However, it does not explore the contextual factors (e.g. institutional distance) affecting the relationship between the affiliate’s membership legitimacy and societal legitimacy. Then more group-level factors are expected to be included and explored with multi-level models in the future studies.

The findings reveal the mechanism of how group affiliates benefiting differently from dual legitimacy to acquire resources in the emerging economies, which also provide a new interpretation for the questions of who benefiting more from the group affiliation, how and why (Carney et al., 2009). This research also explores the moderating roles of task environmental characteristics (environmental uncertainty and environmental munificence) on the affiliate's dual legitimacy and dual resource acquisition, which helps understand why legitimacy building is more important in terms of resource acquisition in the emerging economy characterized by uncertainty and non-munificence.

Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.

PINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.

The prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.

In this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.

This paper examines the impact of the COVID-19 pandemic on low-carbon consumption of dairy products through informational interventions. The empirical findings seek to enlighten developing countries' efforts in coping with climate change and potential dietary transitions.

A randomized controlled trial was designed to examine the effects of purpose-differentiated information interventions on individual dairy consumption. The experiment recruited and randomly assigned 1,002 college students into four groups to receive (or not) environmental or/and health information interventions.

The empirical analysis finds that health and combined information interventions have a positive impact on dairy consumption, while environmental information interventions' effect on dairy consumption is insignificant. In the context of the pandemic, health information interventions positively affected participants' perceptions and preferences for dairy products by delivering knowledge about their role in boosting immunity. However, environmental information interventions failed to do the same things as their insignificant effects on both perception and preference.

Macro-external shocks, such as public health events, may offset the impact of universal information interventions promoting pro-environmental behaviors. For a smooth dietary transition to achieve long-term environmental sustainability, diverse stakeholders must be included in more individualized interventions to guide daily consumption, especially in developing countries with large populations.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

This paper explores the effectiveness of using blockchain technology to solve financial constraints faced by small- and medium-sized suppliers in a capital-constrained supply chain.

To characterize the impact of blockchain on credit period and enterprise credit level, the study formulates a newsvendor model to analyze a supply chain in which a financially constrained supplier sells products to a financially sound manufacturer, subject to uncertain demand. The study investigates three repayment methods: the benchmark case without blockchain and two blockchain-enabled cases with the hybrid repayment mode and single repayment mode (SRM), respectively. The study derives and compares the equilibria under each repayment method to characterize their impact.

When the bank interest rate is low and the carbon cap is also low, choosing to implement blockchain technology leads to higher profitability for the manufacturer than not utilizing it. Within the framework of blockchain technology, when comparing the two repayment models, the manufacturer exhibits a preference for SRM. Furthermore, under specific conditions of the bank interest rate, blockchain technology can effectively facilitate consensus among supply chain members in terms of channel selection.

The results derived in this paper provide novel managerial implications to the capital-constrained members in terms of pricing decisions and order quantity under demand uncertainty considering blockchain technology, which transfers the creditor's rights to the bank and shortens the collection time. In addition, blockchain technology enables efficient and intelligent collaborative development of supply chains, which can reduce carbon emissions during the transportation of goods.

Few studies incorporate blockchain technology into supply chain finance, and this paper considers the credit period and capital's time value for a capital-constrained supplier facing the adoption of blockchain technology within a stochastic demand environment.

This paper aims to investigate the variations in the flow fields induced by transition regions in the windbreak structures between the flat ground and the cutting along a railway and to propose mitigation measures to improve the windproof ability of the windbreak.

The improved delayed detached eddy simulation method was used to simulate the impact of the windbreak transition on flow structures of the high-speed railway under different wind angles, and also the accuracy of the numerical results was validated with those of the wind tunnel test.

The results showed that the original windbreak transition region resulted in a dimensionless peak wind velocity of 0.62 and 0.82 for railway line-1 at wind angles of 90° and 75°, respectively, and the corresponding values were 0.81 and 0.97 for railway line-2. The flow structure analysis revealed the reason for the mismatched height in the transition region, and the right-angle structures of the windbreaks resulted in ineffective protection and sudden changes in the wind speed and direction. Two mitigation measures – oblique structure (OS) and circular curve structure (CCS) transition walls – were developed to reduce the peak wind speed. The OS provided superior protection. The peak value of dimensionless wind velocity was all less than 0.2 for OS and CCS.

The flow field deterioration mechanism induced by the inappropriate form of a windbreak transition at different wind angles was examined, and effective mitigation and improvement measures were proposed and compared with the original transition.

The purpose of this paper is to investigate the forces driving organizational innovation, particularly CEO transformational leadership as it affects external and internal social capital in top management teams.

Survey questionnaires were administered to 90 Chinese top management teams. Structural equation modeling was used to test the hypothesized relationships.

Both internal and external social capital mediated the relationship between transformational leadership and organizational innovation.

Organizations should strengthen internal and external capital of top management teams to reap maximal innovation outcomes from transformational leadership.

The findings contribute to the transformational leadership, social capital, and innovation literature first by showing how leadership influences innovation through largely neglected mechanisms – internal and external social capital. Second, a social capital focus challenges the tacit assumption that transformational leadership has only internal influences by showing that it potentially spills over to the external domain.